Exemplar-based methods for image inpainting achieve impressive results on
some challenging textured images. In this work we propose a general variational framework for non-local image inpainting, from which representative previous inpainting schemes can be derived, in addition to leading to novel ones. We study some of these, and discuss some of
their properties.
The proposed methods require the computation of nearest-neighbor fields (NNF) used to determine correspondences between the inpainting domain and the known part of the image. The PatchMatch algorithm (Barnes et al. 2009) is a collaborative random search strategy that greatly speeds-up the estimation of the NNF. We will present a result on its convergence rate.